Overview

Dataset statistics

Number of variables15
Number of observations4314
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory505.7 KiB
Average record size in memory120.0 B

Variable types

Numeric14
Categorical1

Alerts

recency is highly correlated with num_purchases and 1 other fieldsHigh correlation
avg_days_bw_purchases is highly correlated with num_purchasesHigh correlation
num_purchases is highly correlated with recency and 3 other fieldsHigh correlation
frequency is highly correlated with recency and 1 other fieldsHigh correlation
revenue is highly correlated with num_purchases and 2 other fieldsHigh correlation
avg_ticket is highly correlated with revenue and 1 other fieldsHigh correlation
avg_basket_size is highly correlated with revenue and 1 other fieldsHigh correlation
returns_revenue is highly correlated with avg_return_revenue and 2 other fieldsHigh correlation
avg_return_revenue is highly correlated with returns_revenue and 2 other fieldsHigh correlation
num_returns is highly correlated with returns_revenue and 2 other fieldsHigh correlation
qty_returned is highly correlated with returns_revenue and 2 other fieldsHigh correlation
num_purchases is highly correlated with revenue and 1 other fieldsHigh correlation
revenue is highly correlated with num_purchasesHigh correlation
avg_ticket is highly correlated with avg_basket_sizeHigh correlation
avg_basket_size is highly correlated with avg_ticketHigh correlation
returns_revenue is highly correlated with avg_return_revenue and 1 other fieldsHigh correlation
avg_return_revenue is highly correlated with returns_revenue and 1 other fieldsHigh correlation
num_returns is highly correlated with num_purchasesHigh correlation
qty_returned is highly correlated with returns_revenue and 1 other fieldsHigh correlation
num_purchases is highly correlated with revenueHigh correlation
revenue is highly correlated with num_purchases and 1 other fieldsHigh correlation
avg_ticket is highly correlated with revenue and 1 other fieldsHigh correlation
avg_basket_size is highly correlated with avg_ticketHigh correlation
returns_revenue is highly correlated with avg_return_revenue and 2 other fieldsHigh correlation
avg_return_revenue is highly correlated with returns_revenue and 2 other fieldsHigh correlation
num_returns is highly correlated with returns_revenue and 2 other fieldsHigh correlation
qty_returned is highly correlated with returns_revenue and 2 other fieldsHigh correlation
customer_id is highly correlated with countryHigh correlation
country is highly correlated with customer_id and 3 other fieldsHigh correlation
recency is highly correlated with date_rangeHigh correlation
avg_days_bw_purchases is highly correlated with date_rangeHigh correlation
num_purchases is highly correlated with revenue and 2 other fieldsHigh correlation
date_range is highly correlated with recency and 1 other fieldsHigh correlation
revenue is highly correlated with country and 7 other fieldsHigh correlation
avg_ticket is highly correlated with revenue and 2 other fieldsHigh correlation
avg_basket_size is highly correlated with country and 2 other fieldsHigh correlation
returns_revenue is highly correlated with revenue and 2 other fieldsHigh correlation
avg_return_revenue is highly correlated with revenue and 1 other fieldsHigh correlation
num_returns is highly correlated with country and 4 other fieldsHigh correlation
qty_returned is highly correlated with num_purchases and 5 other fieldsHigh correlation
frequency is highly skewed (γ1 = 58.76792338) Skewed
revenue is highly skewed (γ1 = 21.49337008) Skewed
returns_revenue is highly skewed (γ1 = -31.50396918) Skewed
avg_return_revenue is highly skewed (γ1 = -39.63626209) Skewed
qty_returned is highly skewed (γ1 = -26.03971011) Skewed
customer_id has unique values Unique
avg_days_bw_purchases has 1545 (35.8%) zeros Zeros
returns_revenue has 2824 (65.5%) zeros Zeros
avg_return_revenue has 2824 (65.5%) zeros Zeros
num_returns has 2824 (65.5%) zeros Zeros
qty_returned has 2824 (65.5%) zeros Zeros

Reproduction

Analysis started2022-02-25 15:02:51.569034
Analysis finished2022-02-25 15:04:08.001753
Duration1 minute and 16.43 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

customer_id
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct4314
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15301.828
Minimum12347
Maximum18287
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size33.8 KiB
2022-02-25T12:04:09.069302image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum12347
5-th percentile12618.65
Q113816.25
median15300.5
Q316779.75
95-th percentile17981.4
Maximum18287
Range5940
Interquartile range (IQR)2963.5

Descriptive statistics

Standard deviation1720.158381
Coefficient of variation (CV)0.1124152213
Kurtosis-1.195162781
Mean15301.828
Median Absolute Deviation (MAD)1481.5
Skewness0.00108150282
Sum66012086
Variance2958944.856
MonotonicityNot monotonic
2022-02-25T12:04:09.820517image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
178501
 
< 0.1%
152801
 
< 0.1%
157001
 
< 0.1%
172991
 
< 0.1%
128371
 
< 0.1%
150761
 
< 0.1%
174441
 
< 0.1%
159211
 
< 0.1%
157471
 
< 0.1%
158401
 
< 0.1%
Other values (4304)4304
99.8%
ValueCountFrequency (%)
123471
< 0.1%
123481
< 0.1%
123491
< 0.1%
123501
< 0.1%
123521
< 0.1%
123531
< 0.1%
123541
< 0.1%
123551
< 0.1%
123561
< 0.1%
123571
< 0.1%
ValueCountFrequency (%)
182871
< 0.1%
182831
< 0.1%
182821
< 0.1%
182811
< 0.1%
182801
< 0.1%
182781
< 0.1%
182771
< 0.1%
182761
< 0.1%
182731
< 0.1%
182721
< 0.1%

country
Categorical

HIGH CORRELATION

Distinct35
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size33.8 KiB
United Kingdom
3904 
Germany
 
94
France
 
87
Spain
 
27
Belgium
 
24
Other values (30)
 
178

Length

Max length20
Median length14
Mean length13.3379694
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8 ?
Unique (%)0.2%

Sample

1st rowUnited Kingdom
2nd rowUnited Kingdom
3rd rowFrance
4th rowUnited Kingdom
5th rowUnited Kingdom

Common Values

ValueCountFrequency (%)
United Kingdom3904
90.5%
Germany94
 
2.2%
France87
 
2.0%
Spain27
 
0.6%
Belgium24
 
0.6%
Switzerland20
 
0.5%
Portugal19
 
0.4%
Italy14
 
0.3%
Finland12
 
0.3%
Norway10
 
0.2%
Other values (25)103
 
2.4%

Length

2022-02-25T12:04:10.459477image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
united3906
47.4%
kingdom3904
47.4%
germany94
 
1.1%
france87
 
1.1%
spain27
 
0.3%
belgium24
 
0.3%
switzerland20
 
0.2%
portugal19
 
0.2%
italy14
 
0.2%
finland12
 
0.1%
Other values (30)126
 
1.5%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

recency
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION

Distinct304
Distinct (%)7.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean93.04636069
Minimum1
Maximum374
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size33.8 KiB
2022-02-25T12:04:11.075739image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q118
median51
Q3143
95-th percentile312
Maximum374
Range373
Interquartile range (IQR)125

Descriptive statistics

Standard deviation100.1658707
Coefficient of variation (CV)1.076515728
Kurtosis0.4275857432
Mean93.04636069
Median Absolute Deviation (MAD)40
Skewness1.246531943
Sum401402
Variance10033.20165
MonotonicityNot monotonic
2022-02-25T12:04:11.614002image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2103
 
2.4%
594
 
2.2%
494
 
2.2%
389
 
2.1%
979
 
1.8%
1177
 
1.8%
1874
 
1.7%
871
 
1.6%
1070
 
1.6%
1664
 
1.5%
Other values (294)3499
81.1%
ValueCountFrequency (%)
134
 
0.8%
2103
2.4%
389
2.1%
494
2.2%
594
2.2%
648
1.1%
871
1.6%
979
1.8%
1070
1.6%
1177
1.8%
ValueCountFrequency (%)
37417
0.4%
37317
0.4%
3726
 
0.1%
3703
 
0.1%
3695
 
0.1%
3685
 
0.1%
36710
0.2%
36610
0.2%
3656
 
0.1%
3636
 
0.1%

avg_days_bw_purchases
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct1155
Distinct (%)26.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean50.57101388
Minimum0
Maximum366
Zeros1545
Zeros (%)35.8%
Negative0
Negative (%)0.0%
Memory size33.8 KiB
2022-02-25T12:04:12.156897image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median31.09090909
Q373.2375
95-th percentile184
Maximum366
Range366
Interquartile range (IQR)73.2375

Descriptive statistics

Standard deviation65.3193721
Coefficient of variation (CV)1.291636594
Kurtosis4.650366538
Mean50.57101388
Median Absolute Deviation (MAD)31.09090909
Skewness1.988384569
Sum218163.3539
Variance4266.620371
MonotonicityNot monotonic
2022-02-25T12:04:12.670074image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01545
35.8%
7021
 
0.5%
4618
 
0.4%
5517
 
0.4%
4916
 
0.4%
9116
 
0.4%
4215
 
0.3%
3115
 
0.3%
3515
 
0.3%
2115
 
0.3%
Other values (1145)2621
60.8%
ValueCountFrequency (%)
01545
35.8%
19
 
0.2%
24
 
0.1%
2.8615384621
 
< 0.1%
36
 
0.1%
3.3303571431
 
< 0.1%
3.3513513511
 
< 0.1%
44
 
0.1%
4.1910112361
 
< 0.1%
4.2758620691
 
< 0.1%
ValueCountFrequency (%)
3661
 
< 0.1%
3651
 
< 0.1%
3641
 
< 0.1%
3631
 
< 0.1%
3572
< 0.1%
3561
 
< 0.1%
3552
< 0.1%
3521
 
< 0.1%
3512
< 0.1%
3503
0.1%

num_purchases
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct56
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.260083449
Minimum1
Maximum206
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size33.8 KiB
2022-02-25T12:04:13.266873image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q35
95-th percentile13
Maximum206
Range205
Interquartile range (IQR)4

Descriptive statistics

Standard deviation7.659823284
Coefficient of variation (CV)1.798045361
Kurtosis244.1186295
Mean4.260083449
Median Absolute Deviation (MAD)1
Skewness11.95278092
Sum18378
Variance58.67289274
MonotonicityNot monotonic
2022-02-25T12:04:13.652988image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11493
34.6%
2824
19.1%
3501
 
11.6%
4394
 
9.1%
5237
 
5.5%
6173
 
4.0%
7139
 
3.2%
898
 
2.3%
968
 
1.6%
1055
 
1.3%
Other values (46)332
 
7.7%
ValueCountFrequency (%)
11493
34.6%
2824
19.1%
3501
 
11.6%
4394
 
9.1%
5237
 
5.5%
6173
 
4.0%
7139
 
3.2%
898
 
2.3%
968
 
1.6%
1055
 
1.3%
ValueCountFrequency (%)
2061
< 0.1%
1991
< 0.1%
1241
< 0.1%
971
< 0.1%
911
< 0.1%
901
< 0.1%
861
< 0.1%
731
< 0.1%
622
< 0.1%
601
< 0.1%

date_range
Real number (ℝ≥0)

HIGH CORRELATION

Distinct374
Distinct (%)8.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean187.2227631
Minimum1
Maximum374
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size33.8 KiB
2022-02-25T12:04:14.088067image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile19
Q175.25
median191
Q3286
95-th percentile363
Maximum374
Range373
Interquartile range (IQR)210.75

Descriptive statistics

Standard deviation115.0122756
Coefficient of variation (CV)0.6143071158
Kurtosis-1.330645535
Mean187.2227631
Median Absolute Deviation (MAD)106
Skewness0.01422106554
Sum807679
Variance13227.82354
MonotonicityNot monotonic
2022-02-25T12:04:14.483243image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
36630
 
0.7%
36428
 
0.6%
2526
 
0.6%
6526
 
0.6%
35026
 
0.6%
26725
 
0.6%
35725
 
0.6%
36524
 
0.6%
5424
 
0.6%
2023
 
0.5%
Other values (364)4057
94.0%
ValueCountFrequency (%)
110
0.2%
28
0.2%
310
0.2%
412
0.3%
510
0.2%
67
0.2%
77
0.2%
811
0.3%
96
0.1%
108
0.2%
ValueCountFrequency (%)
37417
0.4%
37321
0.5%
37214
0.3%
3718
 
0.2%
37011
 
0.3%
36911
 
0.3%
36815
0.3%
36720
0.5%
36630
0.7%
36524
0.6%

frequency
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED

Distinct1403
Distinct (%)32.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.04749681833
Minimum0.002673796791
Maximum34
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size33.8 KiB
2022-02-25T12:04:14.936312image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0.002673796791
5-th percentile0.003285698878
Q10.01026962728
median0.0192926045
Q30.03568779144
95-th percentile0.1017463597
Maximum34
Range33.9973262
Interquartile range (IQR)0.02541816416

Descriptive statistics

Standard deviation0.5380913734
Coefficient of variation (CV)11.32899829
Kurtosis3681.775177
Mean0.04749681833
Median Absolute Deviation (MAD)0.01151511188
Skewness58.76792338
Sum204.9012743
Variance0.2895423262
MonotonicityNot monotonic
2022-02-25T12:04:15.319440image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0185185185227
 
0.6%
0.0384615384627
 
0.6%
0.0196078431423
 
0.5%
0.0153846153823
 
0.5%
0.0322580645223
 
0.5%
0.0212765957422
 
0.5%
0.0526315789522
 
0.5%
0.0192307692321
 
0.5%
0.0163934426221
 
0.5%
0.0312520
 
0.5%
Other values (1393)4085
94.7%
ValueCountFrequency (%)
0.00267379679116
0.4%
0.00268096514716
0.4%
0.0026881720436
 
0.1%
0.0027027027032
 
< 0.1%
0.00271002715
 
0.1%
0.0027173913045
 
0.1%
0.002724795649
0.2%
0.00273224043710
0.2%
0.0027397260276
 
0.1%
0.0027548209376
 
0.1%
ValueCountFrequency (%)
341
 
< 0.1%
61
 
< 0.1%
41
 
< 0.1%
26
0.1%
1.51
 
< 0.1%
1.3333333332
 
< 0.1%
16
0.1%
0.66666666673
0.1%
0.55227882041
 
< 0.1%
0.53494623661
 
< 0.1%

revenue
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED

Distinct4228
Distinct (%)98.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1922.493971
Minimum3.75
Maximum278778.02
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size33.8 KiB
2022-02-25T12:04:15.779885image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum3.75
5-th percentile110.8
Q1300.835
median655.335
Q31611.995
95-th percentile5659.487
Maximum278778.02
Range278774.27
Interquartile range (IQR)1311.16

Descriptive statistics

Standard deviation8326.441313
Coefficient of variation (CV)4.33106238
Kurtosis594.9543093
Mean1922.493971
Median Absolute Deviation (MAD)454.265
Skewness21.49337008
Sum8293638.99
Variance69329624.94
MonotonicityNot monotonic
2022-02-25T12:04:16.173058image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
76.324
 
0.1%
113.53
 
0.1%
153
 
0.1%
35.43
 
0.1%
363.653
 
0.1%
79.23
 
0.1%
4403
 
0.1%
193.642
 
< 0.1%
3602
 
< 0.1%
172.252
 
< 0.1%
Other values (4218)4286
99.4%
ValueCountFrequency (%)
3.751
 
< 0.1%
5.91
 
< 0.1%
12.241
 
< 0.1%
12.751
 
< 0.1%
153
0.1%
171
 
< 0.1%
20.82
< 0.1%
25.51
 
< 0.1%
301
 
< 0.1%
30.61
 
< 0.1%
ValueCountFrequency (%)
278778.021
< 0.1%
259657.31
< 0.1%
189735.531
< 0.1%
133007.131
< 0.1%
123638.181
< 0.1%
114505.321
< 0.1%
88138.21
< 0.1%
65920.121
< 0.1%
62924.11
< 0.1%
59419.341
< 0.1%

avg_ticket
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct4222
Distinct (%)97.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean371.2222619
Minimum3.75
Maximum13206.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size33.8 KiB
2022-02-25T12:04:16.554538image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum3.75
5-th percentile88.265
Q1174.41875
median283.27375
Q3421.7894118
95-th percentile892.6525
Maximum13206.5
Range13202.75
Interquartile range (IQR)247.3706618

Descriptive statistics

Standard deviation465.0948215
Coefficient of variation (CV)1.252874273
Kurtosis202.5753972
Mean371.2222619
Median Absolute Deviation (MAD)118.1775
Skewness10.64690937
Sum1601452.838
Variance216313.193
MonotonicityNot monotonic
2022-02-25T12:04:16.969685image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
76.324
 
0.1%
1203
 
0.1%
4403
 
0.1%
79.23
 
0.1%
113.53
 
0.1%
35.43
 
0.1%
174.372
 
< 0.1%
153.2752
 
< 0.1%
172.252
 
< 0.1%
151.052
 
< 0.1%
Other values (4212)4287
99.4%
ValueCountFrequency (%)
3.751
< 0.1%
5.91
< 0.1%
7.51
< 0.1%
9.141
< 0.1%
11.671
< 0.1%
12.241
< 0.1%
12.751
< 0.1%
152
< 0.1%
171
< 0.1%
20.82
< 0.1%
ValueCountFrequency (%)
13206.51
< 0.1%
9338.381
< 0.1%
7178.6333331
< 0.1%
6207.671
< 0.1%
6181.9091
< 0.1%
4873.811
< 0.1%
4366.781
< 0.1%
4327.6216671
< 0.1%
4314.721
< 0.1%
4151.261
< 0.1%

avg_basket_size
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2243
Distinct (%)52.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean200.1733572
Minimum0.25
Maximum7824
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size33.8 KiB
2022-02-25T12:04:17.377036image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0.25
5-th percentile30
Q180.05555556
median140
Q3236.2527778
95-th percentile524.1166667
Maximum7824
Range7823.75
Interquartile range (IQR)156.1972222

Descriptive statistics

Standard deviation269.9096032
Coefficient of variation (CV)1.348379259
Kurtosis193.3956544
Mean200.1733572
Median Absolute Deviation (MAD)70.63068182
Skewness10.0145208
Sum863547.8631
Variance72851.19387
MonotonicityNot monotonic
2022-02-25T12:04:17.762635image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12019
 
0.4%
7218
 
0.4%
6417
 
0.4%
13616
 
0.4%
14416
 
0.4%
4416
 
0.4%
14615
 
0.3%
10015
 
0.3%
10615
 
0.3%
6015
 
0.3%
Other values (2233)4152
96.2%
ValueCountFrequency (%)
0.251
 
< 0.1%
12
 
< 0.1%
24
0.1%
34
0.1%
3.3333333331
 
< 0.1%
47
0.2%
53
0.1%
5.251
 
< 0.1%
5.51
 
< 0.1%
5.6666666671
 
< 0.1%
ValueCountFrequency (%)
78241
< 0.1%
43001
< 0.1%
42801
< 0.1%
3218.4166671
< 0.1%
30281
< 0.1%
29241
< 0.1%
28801
< 0.1%
27081
< 0.1%
2663.9459461
< 0.1%
25291
< 0.1%

avg_unique_prods
Real number (ℝ≥0)

Distinct1001
Distinct (%)23.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21.64808369
Minimum1
Maximum297.8823529
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size33.8 KiB
2022-02-25T12:04:18.133787image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2.977659574
Q19.407142857
median17
Q327.75
95-th percentile56
Maximum297.8823529
Range296.8823529
Interquartile range (IQR)18.34285714

Descriptive statistics

Standard deviation19.44159972
Coefficient of variation (CV)0.8980748595
Kurtosis23.97222811
Mean21.64808369
Median Absolute Deviation (MAD)8.5
Skewness3.301452068
Sum93389.83303
Variance377.9757995
MonotonicityNot monotonic
2022-02-25T12:04:18.591031image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
198
 
2.3%
1398
 
2.3%
1087
 
2.0%
981
 
1.9%
1180
 
1.9%
1474
 
1.7%
672
 
1.7%
872
 
1.7%
772
 
1.7%
570
 
1.6%
Other values (991)3510
81.4%
ValueCountFrequency (%)
198
2.3%
1.21
 
< 0.1%
1.251
 
< 0.1%
1.3333333332
 
< 0.1%
1.58
 
0.2%
1.5454545451
 
< 0.1%
1.5714285711
 
< 0.1%
1.6666666674
 
0.1%
1.8333333331
 
< 0.1%
1.8888888891
 
< 0.1%
ValueCountFrequency (%)
297.88235291
< 0.1%
2591
< 0.1%
2191
< 0.1%
1911
< 0.1%
1711
< 0.1%
1551
< 0.1%
1531
< 0.1%
1482
< 0.1%
1411
< 0.1%
135.33333331
< 0.1%

returns_revenue
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED
ZEROS

Distinct1051
Distinct (%)24.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-50.23773992
Minimum-22998.4
Maximum0
Zeros2824
Zeros (%)65.5%
Negative1490
Negative (%)34.5%
Memory size33.8 KiB
2022-02-25T12:04:19.049481image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-22998.4
5-th percentile-139.5725
Q1-14.4375
median0
Q30
95-th percentile0
Maximum0
Range22998.4
Interquartile range (IQR)14.4375

Descriptive statistics

Standard deviation499.5383175
Coefficient of variation (CV)-9.943487073
Kurtosis1243.448136
Mean-50.23773992
Median Absolute Deviation (MAD)0
Skewness-31.50396918
Sum-216725.61
Variance249538.5306
MonotonicityNot monotonic
2022-02-25T12:04:19.438095image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
02824
65.5%
-12.7520
 
0.5%
-4.9519
 
0.4%
-1517
 
0.4%
-9.9517
 
0.4%
-5.912
 
0.3%
-19.810
 
0.2%
-25.510
 
0.2%
-4.2510
 
0.2%
-3.759
 
0.2%
Other values (1041)1366
31.7%
ValueCountFrequency (%)
-22998.41
< 0.1%
-14688.241
< 0.1%
-8511.151
< 0.1%
-7443.591
< 0.1%
-5228.41
< 0.1%
-4815.261
< 0.1%
-4814.741
< 0.1%
-4486.241
< 0.1%
-44291
< 0.1%
-3677.151
< 0.1%
ValueCountFrequency (%)
02824
65.5%
-0.422
 
< 0.1%
-0.651
 
< 0.1%
-0.951
 
< 0.1%
-1.254
 
0.1%
-1.454
 
0.1%
-1.641
 
< 0.1%
-1.655
 
0.1%
-1.72
 
< 0.1%
-1.791
 
< 0.1%

avg_return_revenue
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED
ZEROS

Distinct1097
Distinct (%)25.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-9.750384134
Minimum-4599.68
Maximum0
Zeros2824
Zeros (%)65.5%
Negative1490
Negative (%)34.5%
Memory size33.8 KiB
2022-02-25T12:04:20.314239image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-4599.68
5-th percentile-29.785
Q1-6.562
median0
Q30
95-th percentile0
Maximum0
Range4599.68
Interquartile range (IQR)6.562

Descriptive statistics

Standard deviation85.59812028
Coefficient of variation (CV)-8.778948512
Kurtosis1977.744808
Mean-9.750384134
Median Absolute Deviation (MAD)0
Skewness-39.63626209
Sum-42063.15715
Variance7327.038195
MonotonicityNot monotonic
2022-02-25T12:04:20.781204image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
02824
65.5%
-12.7523
 
0.5%
-4.9521
 
0.5%
-9.9520
 
0.5%
-1517
 
0.4%
-3.7510
 
0.2%
-4.2510
 
0.2%
-8.259
 
0.2%
-5.99
 
0.2%
-7.59
 
0.2%
Other values (1087)1362
31.6%
ValueCountFrequency (%)
-4599.681
< 0.1%
-1605.0866671
< 0.1%
-1591.21
< 0.1%
-833.251
< 0.1%
-687.821
< 0.1%
-638.61913041
< 0.1%
-5941
< 0.1%
-581.41
< 0.1%
-535.33333331
< 0.1%
-512.91
< 0.1%
ValueCountFrequency (%)
02824
65.5%
-0.422
 
< 0.1%
-0.651
 
< 0.1%
-0.821
 
< 0.1%
-0.951
 
< 0.1%
-1.051
 
< 0.1%
-1.0751
 
< 0.1%
-1.1166666671
 
< 0.1%
-1.255
 
0.1%
-1.381
 
< 0.1%

num_returns
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct57
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.908669448
Minimum0
Maximum223
Zeros2824
Zeros (%)65.5%
Negative0
Negative (%)0.0%
Memory size33.8 KiB
2022-02-25T12:04:21.144069image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile9
Maximum223
Range223
Interquartile range (IQR)1

Descriptive statistics

Standard deviation7.060436262
Coefficient of variation (CV)3.699140397
Kurtosis303.4166822
Mean1.908669448
Median Absolute Deviation (MAD)0
Skewness13.61923014
Sum8234
Variance49.8497602
MonotonicityNot monotonic
2022-02-25T12:04:21.529003image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
02824
65.5%
1473
 
11.0%
2283
 
6.6%
3171
 
4.0%
4117
 
2.7%
583
 
1.9%
652
 
1.2%
751
 
1.2%
841
 
1.0%
1122
 
0.5%
Other values (47)197
 
4.6%
ValueCountFrequency (%)
02824
65.5%
1473
 
11.0%
2283
 
6.6%
3171
 
4.0%
4117
 
2.7%
583
 
1.9%
652
 
1.2%
751
 
1.2%
841
 
1.0%
917
 
0.4%
ValueCountFrequency (%)
2231
< 0.1%
1331
< 0.1%
1121
< 0.1%
1111
< 0.1%
921
< 0.1%
901
< 0.1%
811
< 0.1%
781
< 0.1%
701
< 0.1%
621
< 0.1%

qty_returned
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED
ZEROS

Distinct206
Distinct (%)4.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-25.13908206
Minimum-9360
Maximum0
Zeros2824
Zeros (%)65.5%
Negative1490
Negative (%)34.5%
Memory size33.8 KiB
2022-02-25T12:04:21.894738image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-9360
5-th percentile-57
Q1-3
median0
Q30
95-th percentile0
Maximum0
Range9360
Interquartile range (IQR)3

Descriptive statistics

Standard deviation273.3026969
Coefficient of variation (CV)-10.87162595
Kurtosis787.9373344
Mean-25.13908206
Median Absolute Deviation (MAD)0
Skewness-26.03971011
Sum-108450
Variance74694.36414
MonotonicityNot monotonic
2022-02-25T12:04:22.275368image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
02824
65.5%
-1169
 
3.9%
-2148
 
3.4%
-3105
 
2.4%
-489
 
2.1%
-678
 
1.8%
-561
 
1.4%
-1251
 
1.2%
-744
 
1.0%
-843
 
1.0%
Other values (196)702
 
16.3%
ValueCountFrequency (%)
-93601
< 0.1%
-90141
< 0.1%
-80041
< 0.1%
-44271
< 0.1%
-37681
< 0.1%
-33321
< 0.1%
-28781
< 0.1%
-20221
< 0.1%
-20121
< 0.1%
-17761
< 0.1%
ValueCountFrequency (%)
02824
65.5%
-1169
 
3.9%
-2148
 
3.4%
-3105
 
2.4%
-489
 
2.1%
-561
 
1.4%
-678
 
1.8%
-744
 
1.0%
-843
 
1.0%
-941
 
1.0%

Interactions

2022-02-25T12:04:01.544901image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-25T12:02:58.488143image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-25T12:03:04.054382image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-25T12:03:09.316329image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-25T12:03:13.871848image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-25T12:03:18.803906image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-25T12:03:23.785654image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-25T12:03:28.926193image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-25T12:03:33.792795image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-25T12:03:39.336750image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-25T12:03:44.575385image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-25T12:03:48.739736image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-25T12:03:52.868099image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-25T12:03:57.489312image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-25T12:04:01.819850image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-25T12:02:59.075403image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-25T12:03:04.351643image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-25T12:03:09.625887image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-25T12:03:14.471350image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-25T12:03:19.106675image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-25T12:03:24.251700image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-25T12:03:29.200438image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-25T12:03:34.105814image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-25T12:03:39.905745image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-25T12:03:44.872183image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-25T12:03:49.041945image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-25T12:03:53.164723image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-25T12:03:57.787244image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-25T12:04:02.121220image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-25T12:02:59.379365image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-25T12:03:04.756382image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-25T12:03:09.927904image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-25T12:03:14.931707image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-25T12:03:19.442501image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-25T12:03:24.586771image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-25T12:03:29.492399image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-25T12:03:34.401831image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-25T12:03:40.316902image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-25T12:03:45.180541image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-25T12:03:49.333919image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-25T12:03:53.502032image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-25T12:03:58.078656image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-25T12:04:02.429366image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-25T12:02:59.699635image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-25T12:03:05.205615image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-25T12:03:10.267110image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-25T12:03:15.430290image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-25T12:03:19.794369image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-25T12:03:25.057795image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-25T12:03:29.815730image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-25T12:03:34.708387image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-25T12:03:40.783616image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-25T12:03:45.474836image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-25T12:03:49.671832image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-25T12:03:53.852780image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-25T12:03:58.423426image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-25T12:04:02.724868image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-25T12:03:00.020567image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-25T12:03:05.684502image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-25T12:03:10.624576image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-25T12:03:15.726183image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-25T12:03:20.077422image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-25T12:03:25.509232image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-25T12:03:30.112024image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-25T12:03:34.978481image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-25T12:03:41.204597image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-25T12:03:45.745872image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-25T12:03:49.952579image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-25T12:03:54.125066image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-25T12:03:58.681781image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-25T12:04:03.135454image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-25T12:03:00.433649image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-25T12:03:06.091464image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-25T12:03:11.028879image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-25T12:03:16.103270image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-25T12:03:20.588707image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-25T12:03:25.877209image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-25T12:03:30.404564image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-25T12:03:35.349317image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-25T12:03:41.637135image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-25T12:03:46.051159image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-25T12:03:50.263325image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-25T12:03:54.815497image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-25T12:03:58.954498image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-25T12:04:03.581270image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-25T12:03:00.879254image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
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2022-02-25T12:03:55.390909image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
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2022-02-25T12:03:17.043065image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
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2022-02-25T12:03:31.434537image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-25T12:03:36.557913image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
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2022-02-25T12:03:46.995616image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-25T12:03:51.201656image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-25T12:03:55.700883image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-25T12:03:59.878404image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-25T12:04:04.498243image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-25T12:03:02.131274image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
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2022-02-25T12:03:12.354959image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
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2022-02-25T12:04:01.263513image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Correlations

2022-02-25T12:04:22.587882image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-02-25T12:04:23.015331image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-02-25T12:04:23.460315image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-02-25T12:04:23.915383image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-02-25T12:04:06.172513image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
A simple visualization of nullity by column.
2022-02-25T12:04:07.201528image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

customer_idcountryrecencyavg_days_bw_purchasesnum_purchasesdate_rangefrequencyrevenueavg_ticketavg_basket_sizeavg_unique_prodsreturns_revenueavg_return_revenuenum_returnsqty_returned
017850United Kingdom3731.00000034134.0000005288.63155.54794148.3714298.735294-102.58-6.83866715.0-40.0
113047United Kingdom5752.83333393170.0283913089.10343.23333384.68750019.000000-143.49-6.23869623.0-35.0
212583France326.500000153710.0404316629.34441.956000292.82352915.466667-76.04-25.3466673.0-50.0
313748United Kingdom9692.66666752780.017986948.25189.65000087.8000005.6000000.000.0000000.00.0
415100United Kingdom33420.0000003400.075000635.10211.7000009.6666671.000000-240.90-80.3000003.0-22.0
515291United Kingdom2626.769231143480.0402304551.51325.107857109.1052637.285714-71.79-11.9650006.0-29.0
614688United Kingdom819.263158213660.0573775107.38243.208571119.33333315.285714-523.49-16.35906332.0-399.0
717809United Kingdom1739.666667123570.0336135344.85445.404167144.0000005.083333-67.06-33.5300002.0-41.0
815311United Kingdom14.191011913730.24396859419.34652.959780319.66101725.901099-1348.56-12.040714112.0-474.0
916098United Kingdom8847.66666772860.0244762005.63286.51857187.5714299.4285710.000.0000000.00.0

Last rows

customer_idcountryrecencyavg_days_bw_purchasesnum_purchasesdate_rangefrequencyrevenueavg_ticketavg_basket_sizeavg_unique_prodsreturns_revenueavg_return_revenuenum_returnsqty_returned
430416000United Kingdom30.0331.00000012393.704131.2333331703.3333333.00.000.000.00.0
430515195United Kingdom30.0130.3333333861.003861.0000001404.0000001.00.000.000.00.0
430614087United Kingdom30.0130.333333181.67181.670000125.00000061.0-12.75-12.751.0-1.0
430714204United Kingdom30.0130.333333161.03161.03000082.00000036.00.000.000.00.0
430815471United Kingdom30.0130.333333469.48469.480000266.00000067.00.000.000.00.0
430913436United Kingdom20.0120.500000196.89196.89000076.00000012.00.000.000.00.0
431015520United Kingdom20.0120.500000343.50343.500000314.00000018.00.000.000.00.0
431113298United Kingdom20.0120.500000360.00360.00000096.0000002.00.000.000.00.0
431214569United Kingdom20.0120.500000227.39227.39000079.00000010.00.000.000.00.0
431312713Germany10.0111.000000794.55794.550000505.00000037.00.000.000.00.0